Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JOIV : International Journal on Informatics Visualization Jurnal Senopati : Sustainability, Ergonomics, Optimization, and Application of Industrial Engineering
Zahira, Aulia Dihas
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Development of Automatic Object Detection and IoT for Garbage Pickup Assignment Problem Bayu Setyawan, Erlangga; Novitasari, Nia; Zahira, Aulia Dihas
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2740

Abstract

Waste management remains a challenge in certain cities, particularly in allocating fleets responsible for collecting garbage from temporary disposal sites. Inadequate planning can lead to the accumulation of substantial waste piles. This study aims to enhance truck assignment by considering truck capacity and the collection route. The assignment process incorporates the fundamental concept of the transportation problem, precisely the northwest corner method. The volume of waste transported aligns with the resident or industrial population within the designated service area. The waste generation capacity determines the future fleet and quantity, forming a crucial element of the ensuing distribution channel. A monitoring system integrating object detection and the Internet of Things (IoT) has been devised to ensure effective garbage collection. Cameras strategically positioned at temporary disposal sites transmit real-time images. The system evaluates garbage collection capacity through object detection facilitated by neural network training. The research outcomes demonstrate the system's capability to identify waste pile levels and validate the garbage pickup process by the designated fleet. Future research should focus on assignment and scheduling in waste transportation, enabling fleet allocation within specific timeframes. Additionally, an object detection algorithm refinement is necessary for more precise identification of waste pile locations.
Prediksi Preferensi Waktu Pengiriman Pelanggan Untuk Optimalisasi Layanan Logistik Menggunakan Decision Tree Yulianti, Femi; Zahira, Aulia Dihas
Jurnal SENOPATI : Sustainability, Ergonomics, Optimization, and Application of Industrial Engineering Vol 7, No 2 (2026): Jurnal SENOPATI Vol 7, No 2 (in progress)
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.senopati.2026.v7i2.8113

Abstract

In the era of customer-centric logistics, the ability to predict delivery time preferences is critical to enhancing service quality and operational efficiecy. This study proposes a predictive analytics framework to classify customer-preferred delivery times – morning, afternoon, evening, or night – using historical logistics data and customer perception analysis. Utilizing the Amazon delivery dataset, categorical time intervals were derived through timestamp transformation, enabling classification modeling with three machine learning algorithms Naive bayes, Logistic regression, and Decision tree. Among these, the Decision tree algorithm yielded the best performance, achieving an accuracy of 77% and a macro F1-score of 0.77. Further analysis revealed that traffic conditions, vehicle types, and product categories were the most influential features in predicting delivery time preference. Survei results corroborated the model’s findings, with customer responses highlighting traffic and delivery timing as top priorities for service satisfaction. This research demonstrates the integration of data-driven modeling with customer insights to support decision-making in last-mile logistics. The findings can guide logistics providers in designing adaptive, preference-based delivery schedules that improve service realiability and user experience.Keywords: predictive analytics, delivery time preference, decision tree, last-mile logistics, customer satisfaction, machine learning.
Co-Authors Erlangga Bayu Setyawan Nia Novitasari Yulianti, Femi